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When can we ignore measurement error in the running variable? (replication data)
In many applications of regression discontinuity designs, the running variable used to assign treatment is only observed with error. We show that, provided the observed running... -
Subspace shrinkage in conjugate Bayesian vector autoregressions (replication ...
For the empirical exercise we use quarterly macroeconomic data for the US, obtained from the FRED-QD database (https://research.stlouisfed.org/econ/mccracken/fred-databases/).... -
Local Fiscal Equity in the USA (replication data)
Unlike many other countries, the United States does not have a comprehensive federal transfer scheme for explicit fiscal equalization but rather employs an array of categorical... -
Too Much of a Good Thing? Households’ Macroeconomic Conditions and Credit Dyn...
Favorable macroeconomic conditions, accompanied by optimistic consumer confidence, can stimulate and shape households' expectations in such a way that they gradually extrapolate... -
The Macroeconomic Determinants of House Prices and Rents
Based on panel error correction models for a sample of up to 21 countries this paper analyses the macroeconomic determinants of house prices and rents. In accordance with the... -
A comparison of approaches to select the informativeness of priors in BVARs
Vector autoregressions (VARs) are richly parameterized time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, in small... -
Après-ski: The Spread of Coronavirus from Ischgl through Germany
The Austrian ski resort of Ischgl is commonly claimed to be ground zero for the diffusion of the SARS-CoV-2 virus in the first wave of infections experienced by Germany. Drawing... -
Triplets, Quads and Quints: Estimating Disaggregate Trade Elasticities with D...
Trade elasticities are a crucial variable for research on international trade. Caliendo and Parro (2015) provide a novel method to estimate trade elasticities which is based on... -
Early prediction of university dropouts - a random forest approach
We predict university dropout using random forests based on conditional inference trees and on a broad German data set covering a wide range of aspects of student life and study... -
Using a Bayesian Structural Time–Series Model to Infer the Causal Impact on C...
The Bayesian structural time series model, used in conjunction with a state–space model, is a novel means of exploring the causal impact of a policy intervention. It extends the...
